Dissecting reversible and irreversible single cell state transitions from gene regulatory networks
Daniel A Ramirez1,2, Mingyang Lu3,4
1Center for Theoretical Biological Physics, Northeastern University, Boston, MA, 02115, USA.
Molecular Systems Biology
|February 9, 2026
Summary
We developed a new computational method, state transition inference using cross-cell correlations (STICCC), to predict cell state transitions using gene expression data. STICCC reveals how gene regulatory networks drive these dynamic cell changes.
Area of Science:
- Computational Biology
- Systems Biology
- Genomics
Background:
- Understanding cell state transitions is crucial in biology.
- Gene regulatory mechanisms govern these dynamic processes.
- Existing methods like pseudotime and RNA velocity offer limited insights.
Purpose of the Study:
- To develop a novel computational method for predicting cell state transitions.
- To infer reversible and irreversible cell fate decisions at single-cell resolution.
- To connect gene regulatory interactions with system dynamics.
Main Methods:
- State transition inference using cross-cell correlations (STICCC) method.
- Utilizes single-cell gene expression data and gene regulatory interactions.
- Exploits gene expression time delays to infer past and future states.
Main Results:
- STICCC accurately predicts cell state transitions.
- Inferred vector fields capture basins of attraction and irreversible fluxes.
- Reveals the influence of network interactions on cell fate decisions.
Conclusions:
- STICCC provides complementary insights beyond pseudotime and RNA velocity.
- The method enhances understanding of gene regulation in cell state transitions.
- STICCC is a valuable tool for studying dynamic biological systems.
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